The subject matter disclosed herein relates to elevator systems. More specifically, the subject matter disclosed herein relates to an elevator control system.
Elevator control systems typically cannot detect the presence of arriving passengers until they press a hall call button, enter a destination via a keypad/touchscreen or use their security credential at a turnstile or reader. In the case of up peak and down peak traffic, there is a steady increase in passenger arrival rates, and these peaks typically occur during the same periods of time each day and normally involve the lobby floor(s). The elevator control system can thus be programmed and configured in advance to handle these peak traffic periods, e.g., by parking more than one car at the lobby and dragging cars back to the lobby after they finish servicing passenger demand. Because these peak periods occur at regular times each day, a system capable of learning by analysing historical traffic patterns could deduce when and where these peak periods occur.
The random arrival or departure of large groups of passengers from one or more non-lobby floors in a building, such as those used for meeting rooms, can provide challenges for an elevator control system. The passengers arrive at or depart from these floors in a relatively short space of time, which provides a short duration peak in activity that could potentially cause long waiting times for passengers as the elevator system attempts to provide sufficient carrying capacity to service this unexpected demand. The system has no way of knowing when these bursts will occur, and so it cannot be pre-programmed to anticipate them. A learning system might be able to deduce which floors these peaks occur at, but since most meetings generally do not happen at consistent times, there would be a tendency to over-compensate and either assign too many or too few cars to service these floors.